Method, apparatus, and system for determining a ground control point from image data using machine learning

ABSTRACT

An approach is provided for determining a ground control point from image data using machine learning. The approach, for example, involves selecting an feature based determining that the feature meets one or more properties for classification as a machine learnable feature. The approach also involves retrieving a plurality of ground truth images depicting the feature. The plurality of ground truth images is labeled with known pixel location data of the feature as respectively depicted in each of the plurality of ground truth images. The approach further involves training a machine learning model using the plurality of ground truth images to identify predicted pixel location data of the ground control point as depicted in an input image.

BACKGROUND

Modern location-based services and applications (e.g., autonomousdriving) are increasingly demanding highly accurate and detailed digitalmap data (e.g., centimeter-level accuracy or better). To achieve suchlevels of accuracy, map service providers have traditionally used groundcontrol points to precisely align and/or correct digital map data fromdifferent sources. Ground control points, for instance, are identifiablepoints on the Earth's surface that have precise three-dimensionallocation (e.g., latitude, longitude, and elevation). Traditionally,generating ground control points has been a manual effort that requiresdeploying ground surveyors to the locations of ground control points tomake manual measurements. This traditional approach, however, is laborintensive can does not scale well when available manual resources arelimited. Accordingly, map service providers face significant technicalchallenges to automatically designating and measuring ground controlpoints at the target levels of accuracy.

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for an approach for automatically determiningground control points from image data using machine learning.

According to one embodiment, a computer-implemented method fordetermining a ground control point from image data comprises selecting afeature based determining that the feature meets one or more propertiesfor classification as a machine learnable feature. The method alsocomprises retrieving a plurality of ground truth images depicting thefeature. The plurality of ground truth images is labeled with knownpixel location data of the feature as respectively depicted in each ofthe plurality of ground truth images. The method further comprisestraining a machine learning model using the plurality of ground truthimages to identify predicted pixel location data of the ground controlpoint as depicted in an input image.

According to another embodiment, an apparatus for determining a groundcontrol point from image data comprises at least one processor, and atleast one memory including computer program code for one or morecomputer programs, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause, at least in part,the apparatus to select a feature based determining that the featuremeets one or more properties for classification as a machine learnablefeature. The apparatus is also caused to retrieve a plurality of groundtruth images depicting the feature. The plurality of ground truth imagesis labeled with known pixel location data of the feature as respectivelydepicted in each of the plurality of ground truth images. The apparatusis further caused to train a machine learning model using the pluralityof ground truth images to identify predicted pixel location data of theground control point as depicted in an input image.

According to another embodiment, a non-transitory computer-readablestorage medium for determining a ground control point from image datacarries one or more sequences of one or more instructions which, whenexecuted by one or more processors, cause, at least in part, anapparatus to select a feature based determining that the feature meetsone or more properties for classification as a machine learnablefeature. The apparatus is also caused to retrieve a plurality of groundtruth images depicting the feature. The plurality of ground truth imagesis labeled with known pixel location data of the feature as respectivelydepicted in each of the plurality of ground truth images. The apparatusis further caused to train a machine learning model using the pluralityof ground truth images to identify predicted pixel location data of theground control point as depicted in an input image.

According to another embodiment, an apparatus for determining a groundcontrol point from image data comprises means for selecting a featurebased determining that the feature meets one or more properties forclassification as a machine learnable feature. The apparatus alsocomprises means for retrieving a plurality of ground truth imagesdepicting the feature. The plurality of ground truth images is labeledwith known pixel location data of the feature as respectively depictedin each of the plurality of ground truth images. The apparatus furthercomprises means for training a machine learning model using theplurality of ground truth images to identify predicted pixel locationdata of the ground control point as depicted in an input image.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing a method of the claims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1 is a diagram of a system capable of determining a ground controlpoint from image data, according to one embodiment;

FIG. 2 is a diagram of components of a machine learning system capableof determining a ground control point from image data, according to oneembodiment;

FIG. 3 is a flowchart of a process for determining a ground controlpoint from image data, according to one embodiment;

FIG. 4 is a diagram illustrating example intersection features,according to one embodiment;

FIGS. 5A-5C are diagrams illustrating example imagery of intersectionfeatures, according to one embodiment;

FIGS. 6A and 6B are diagrams illustrating multiple images of the sameintersection feature, according to one embodiment;

FIG. 7 is a flowchart of a process for generating prediction locationdata for a ground control point using a trained machine learning model,according to one embodiment;

FIG. 8 is a diagram illustrating a workflow for using ground controlpoints, according to one embodiment;

FIG. 9 is a diagram of a geographic database, according to oneembodiment;

FIG. 10 is a diagram of hardware that can be used to implement anembodiment;

FIG. 11 is a diagram of a chip set that can be used to implement anembodiment; and

FIG. 12 is a diagram of a mobile terminal (e.g., handset) that can beused to implement an embodiment.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, apparatus, and computer program for determining aground control point from image data using machine learning aredisclosed. In the following description, for the purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the embodiments of the invention. It isapparent, however, to one skilled in the art that the embodiments of theinvention may be practiced without these specific details or with anequivalent arrangement. In other instances, well-known structures anddevices are shown in block diagram form in order to avoid unnecessarilyobscuring the embodiments of the invention.

FIG. 1 is a diagram of a system capable of determining a ground controlpoint from image data, according to one embodiment. As indicated above,many location-based services and applications rely on accurate map data.For example, automated driving is quickly becoming a reality followingadvances in machine learning, computer vision, and compute power. Theability to perceive the world with an accurate semantic understandingenables vehicles (e.g., an autonomous vehicle 101) to obey driving rulesand avoid collisions. As these perceptual abilities have improved, sotoo has the need for highly accurate and up-to-date maps. Path planning,for instance, requires knowledge of what to expect beyond a vehicle101's perceptual horizon, and driving in complicated urban environmentswith many occluding objects requires a knowledge of what cannot bedetected by onboard sensors.

In response, map service providers (e.g., operating a mapping platform103) are creating the most accurate and up-to-date high-resolution mapfor automated driving (e.g., a geographic database 105). To facilitateand/or monitor the accuracy of digital map data stored in the geographicdatabase 105, map service providers can designate ground control points.In one embodiment, ground control points are defined as identifiablepoints on the Earth's surface that have precise location (e.g., in theform of <Latitude, Longitude, Elevation>) associated with them. Thesepoints play a vital role in being able to measure the quality andcorrection of different data sources.

In other embodiments, ground control points find additional applicationsin camera pose refinement of satellite, aerial and ground imagery, andhence provide for increased position fidelity for location datadetermined from these data sources. In turn, any derived products likebuilding polygons, map objects made from these data sources inherit theaccuracy. In addition, ground control points can also serve thelocalization of the automated car use case where they can be geocodedlocalization objects that car can measure its position with respect to.

Traditionally, ground control points are collected by ground surveyorswho go out in the field and use instruments like a theodolite, measuringtape, three-dimensional (3D) scanner, satellite-based location sensors(e.g., GPS/GNSS), level and rod, etc. to measure the locations of groundcontrol points with respect to the locations of distinguishablelandmarks on the Earth (e.g. parts of signs, barriers, buildings, roadpaint, etc.). Collecting each ground control point using traditionalmanual means requires a substantial amount of infrastructure and manualresources. The problems become even more pronounced if the groundcontrol points need to be measured on the road (e.g., for map making usecases) since special access permissions need to be obtained from thegovernment or other responsible authorities. Because of theinfrastructure and resource burden, the process of obtaining groundcontrol points using traditional means is not scalable if they need tobe used in map making and evaluation process.

To complicate the process further, ground control points are valid forunpredictable periods of time. For example, a previously measure groundcontrol point can become invalid or obsolete if the feature or object(e.g., a building, lane marking, etc.) on which the ground control pointis based changes, for instance, due to construction, paintdeterioration, and/or other changes to the environment. Other changes,for instance, can include to shifts in tectonic plates or othergeological movements that shift the location of ground control points bya couple of centimeters or more per year. For high definition map use(e.g., with centimeter level accuracy), those micro changes in groundcontrol points can have an effect on the accuracy of digital maps.Accordingly, map service providers face significant technical challengesto determining ground control points that can scale (e.g., withincreased map coverage) given limited available resources and that canbe updated at a frequency sufficient to reduce the probability of aground control point becoming invalid or obsolete below a targetthreshold.

To address these technical challenges and problems, the system 100 ofFIG. 1 introduces a capability to identify ground control points thatare learnable from top down imagery by machine learning systems (e.g.,using the machine learning system 107 of the mapping platform 103 incombination with the computer vision system 109). In one embodiment, thesystem 100 can mark or label these learnable ground control points(e.g., an intersection of ground paint lines) in a large of set oftraining images to train the machine learning system 107. The trainedmachine learning system 107 can then be used to automatically find thelocations of all such ground control points in new images. By way ofexample, such imagery or image data can be obtained from differentsources such as but not limited to satellites, airplanes, drones, and/orother aerial vehicles. In one embodiment, the system 100 selects the mapfeatures for use by the machine learning system 107 (e.g., for training,evaluation, and/or prediction) based on determining whether the mapfeatures are “learnable” by the machine learning system 107. Learnable,for instance, refers to whether the ground control point/map feature canbe used to train the machine learning system 107 to make predictions ofa corresponding location of the ground control point as depicted inimage data (e.g., top-down imagery).

In one embodiment, to determine whether a ground control point/mapfeature is learnable, the system 100 can determine whether the mapfeature satisfies any combination of designated properties such as butnot limited to:

-   -   The ground control point/map feature should have a consistent        definition so that the machine learning system 107 (e.g.,        machine learning models) can learn it;    -   The ground control point/map feature should be uniquely        identifiable;    -   The ground control point/map feature should be spatially sparse        so the correspondence task can be simplified; and    -   The definition of the ground control point/map feature should be        generalizable enough be applicable in different parts or regions        of the world.

After selecting learnable map features/ground control points accordingto the criteria above, the system 100 can label image data (e.g.,top-down imagery) depicting examples of the selected ground controlpoints to generate a set of training images. Labeling, for instance,includes identifying (e.g., using a human labeler) the pixel locationswithin each image corresponding to the learnable ground control pointspresent in the image. The system 100 can then use the labeled trainingimages to train the machine learning system 107 to predict all suchground control points in the input image data (e.g., by predicting thepixel locations or data indicating the pixel locations of the groundcontrol points in the input images).

In one embodiment, the system 100 uses the trained machine learningsystem 107 to automatically label the ground control points in newimagery depicting areas to be mapped or analyzed. Then, the 3D positionof the ground control point/map feature can be estimated from multipletop-down views, with corresponding ground control points labeled in twoor more images. For example, the system 100 can determine pixelcorrespondences between the ground control points labeled in each of theimages. The 3D position of the ground control points can then bedetermined via a triangulation process from the pixel correspondences ofeach ground control point in combination with a camera model or camerapose (e.g., camera position, pointing direction, etc.) of the camerasystem used to capture the imager. In addition, different image sources(e.g., satellites, airplanes, drones, etc.) provide imagery withdifferent qualities and resolutions and hence the uncertainty/errorassociated with the generated ground control points from them will bedifferent and can also be computed. The embodiments for determiningground control points from image data using machine learning aredescribed in more detail below.

As shown in FIG. 1, in one embodiment, the mapping platform 103 includesthe machine learning system 107 and computer vision system 109 fordetermining ground control points from image data. FIG. 2 is a diagramof components of the machine learning system 107 that includes one ormore components for determining ground control points from image dataaccording to the various embodiments described herein. It iscontemplated that the functions of these components may be combined orperformed by other components of equivalent functionality. In thisembodiment, the machine learning system 107 includes a feature module201, a training module 203, a prediction module 205, and a triangulationmodule 207. The above presented modules and components of the machinelearning system 107 can be implemented in hardware, firmware, software,or a combination thereof. Though depicted as a component of the mappingplatform 103 in FIG. 1, it is contemplated that the machine learningsystem 107 may be a separate entity or may be implemented as a module ofany other component of the system 100 (e.g., a component of the computervision system 109, services platform 111, services 113 a-113 n (alsocollectively referred to as services 113), vehicle 101, a user equipment(UE) 115, application 117 executing on the UE 115, etc.). In anotherembodiment, one or more of the modules 201-207 may be implemented as acloud based service, local service, native application, or combinationthereof. The functions of the machine learning system 107 and themodules 201-207 are discussed with respect to FIGS. 3-8 below.

FIG. 3 is a flowchart of a process 300 for determining a ground controlpoint from image data, according to one embodiment. More specifically,the embodiments of the process 300 can be used to train a machinelearning model of the system 100 to predict the locations and/or othercharacteristics of ground control points depicted in image data. Invarious embodiments, the machine learning system 107 and/or any of themodules 201-207 of the machine learning system 107 may perform one ormore portions of the process 300 and may be implemented in, forinstance, a chip set including a processor and a memory as shown in FIG.11. As such, the machine learning system 107 and/or the modules 201-207can provide means for accomplishing various parts of the process 300, aswell as means for accomplishing embodiments of other processes describedherein in conjunction with other components of the system 100. Althoughthe process 300 is illustrated and described as a sequence of steps, itscontemplated that various embodiments of the process 300 may beperformed in any order or combination and need not include all of theillustrated steps.

In step 301, the feature module 201 selects or receives an input (e.g.,from a user or system administrator) for selecting features that are tobe used for designating ground control points. Features, for instance,refer to any physical feature identifiable on the ground as possiblecandidates. However, not all of the available candidate features aresuitable for machine learning (i.e., “learnable”) as discussed above.Accordingly, in one embodiment, the feature selection module 201 canevaluate or receive input specifying an evaluation of the candidatefeatures for selection as ground control points.

As previously discussed, in one embodiment, the feature module 201 canuse one or more designated properties for determining whether acandidate feature is learnable and therefore can be selected as a groundcontrol point for machine learning. For example, the designatedproperties can include but are not limited to: (1) having a consistentdefinition, (2) being uniquely identifiable, (3) having spatialsparsity, and/or (4) being generalizable across different geographicregions. In one embodiment, the feature selection process can beperformed as part of an initial set up phase. By way of example, thecategory of curvilinear geometry intersections includes physicalfeatures (e.g., intersection features) which generally meet the abovecriteria to be candidates as machine learnable ground control points.Curvilinear geometry intersection features are features defined bylines, markings, structures, etc. that are found at roadwayintersections. The features can also include any geometric arrangementof the features (e.g., line intersections, angles, boundaries, etc.).

In one embodiment, the intersection features are those features orgeometric arrangements of the features that are visible in top-downimagery. It is noted that intersection features or curvilinear geometryfeatures are provided as examples of physical features that can meet thelearnable criteria described above. Although the various embodiments arediscussed with respect to intersection feature, it is contemplated thatany feature that meets the above criteria can be used as learnableground control points according to the embodiments described herein. Assuch, the computer vision system 109 can more easily identify thesefeatures using object recognition on the corresponding images. Top-downimagery refers to images or image data that are captured from anoverhead or aerial perspective so that the camera is pointed downtowards the intersection or ground level from an overhead height. Theaxis of the pointing direction of the camera can vary from a directoverhead (e.g., perpendicular angle) or to an oblique angle from eitherside. In one embodiment, the camera pose or position data can beprovided with the imagery and then refined to greater accuracy usingground control points. Other camera attributes (e.g., focal length,camera type, etc.) and/or environmental attributes (e.g., weather, timeof day, etc.) can be provided with the imagery.

FIG. 4 is a diagram illustrating example intersection features that canbe found at a typical intersection, according to one embodiment. Theexample of FIG. 4 illustrates a schematic drawing of a typicalintersection 400 at which intersection features created by variousgeometries of the lane lines, crosswalks, bus stops, and/or any otheridentifiable object or marking found at the intersection 400. Anintersection refers, for instance, to a geographic area at which two ormore road segments intersect, converge, and/or diverge. As shown,intersection features in the category of curvilinear geometry includebut are not limited to:

-   -   (1) Points 401 a-401 c at which a lane boundary (e.g., lane line        or marking) meets a crosswalk;    -   (2) Points 403 a and 403 that correspond to the corners of road        markings indicating a bus stop;    -   (3) Points 405 a-405 d that correspond to the corners of a        crosswalk;    -   (4) Points 407 a and 407 b that are the top of gore points        touching a crosswalk or limit lines (e.g., lines designating the        limit or boundaries of other features such as lanes); and    -   (5) Point 409 at which a limit line meets a lane boundary.

The intersection features identified above typically meet the criteriaor properties for being classified as learnable according to theembodiments described herein. For example, the property of having a“consistent definition” (e.g., see designated property item (1) above)indicates whether a feature description can be consistently applied andlearned by the machine learning system 107. This consistency can dependon whether the machine learning system 107 can identify the feature orground control point based on its definition with accuracy above athreshold value. For example, a map or intersection feature can bedefined using a descriptive property such as “a lane boundary with acrosswalk”. This means that the map feature/ground control point wouldcorrespond to the location or point at which a lane line meets a crosswalk line. In one embodiment, the consistency of the definition of theground control point can be measured by determining whether the featuremodule 201 can correctly identify the feature in ground truth imagesabove a threshold accuracy level (e.g., more than 80% correct). Map orintersection features that meet this threshold can then be classified ashaving a consistent definition that satisfies the property or criterion.

In one embodiment, the intersection feature/ground control point isselected so that the intersection feature is uniquely identifiable fromamong other intersection features from the category of curvilineargeometry intersection features (e.g., see designated property item (2)above). In other words, a single feature should only be classified underone feature definition category. For example, if a feature that isclassified as “a lane boundary with a crosswalk” should also not satisfythe definition for being a “gore point” or vice versa. In oneembodiment, if a feature is not uniquely identifiable, the featuremodule 201 can classify the feature as not learnable.

In another embodiment, the intersection feature is selected based ondetermining that the intersection feature has a spatial sparsity thatmeets a sparsity criterion (e.g., see designated property item (3)above). Under this property, the feature module 201 can determine thenumber of same intersection feature occurring in a known geographic tocalculate the spatial sparsity of the feature. The feature module 201can then classify the feature as learnable if the sparsity (e.g., numberfeatures per area) is below the sparsity criterion or threshold. Incontrast, features that repeat often within an designated area (i.e.,not sparse or appear in numbers greater than the sparsity threshold) arenot well suited as ground control points because they can be moredifficult to uniquely identify and match as against known ground controlpoints. For example, features such as dashes of a lane line, stripes ina crosswalk, multiple line paint intersections in restricted zones,zebra stripes, etc. that repeat often over small distances can be poorground control point candidates.

In yet another embodiment, the feature module 201 can determine whetherthe intersection feature is applicable to a plurality of differentgeographic regions (e.g., see designated property item (1) above). Inother words, features are learnable if they are likely to appear acrossvarious regions of the world. If a selected feature or ground controlpoint is specific only to a particular area, then the resulting trainedmachine learning model would be applicable only to the particular area.To provide a more general machine learning system 107, the featuremodule 201 can select only those features that occur in all or amajority (e.g., greater than a threshold percentage) of the areas orregions that are of interest.

FIGS. 5A-5C illustrate example imagery of some of the intersectionfeatures illustrated in FIG. 4, according to one embodiment. Forexample, FIG. 5A illustrates top-down imagery 500 that depicts groundcontrol points 501 a-501 e at which a lane boundary meets a crosswalk.FIG. 5B illustrates to-down imagery 520 that depicts ground controlpoints 521 a and 521 b that are crosswalk corners. FIG. 5C illustratestop-down imagery 540 that depicts ground control points 541 a and 541 bat which a limit line meets a lane line. Each of the ground controlpoints illustrated in FIGS. 5A-5C are an intersection features from acategory of curvilinear geometry intersection features that are avisible feature of a roadway intersection (e.g., visible from a top-downimagery perspective).

After selecting the features that are to be designated as ground controlpoints, the feature module 201 can label and/or retrieve a plurality ofground truth images depicting the intersection feature (step 303). Inone embodiment, the plurality of ground truth images is labeled withknown pixel location data of the intersection as respectively depictedin each of the plurality of ground truth images. The known pixellocation data indicate which pixel(s) of a ground truth image correspondto ground control points that are present in the image. As previouslydescribed, the known pixel location data can be used to determined pixelcorrespondences between multiple images to determine real worldthree-dimensional locations of the ground control point (e.g.,intersection feature) comprising a latitude, longitude, and elevation.The ground truth images can also include multiple images of the sameground control point or learnable feature (e.g., captured at differenttimes, from different sources, etc.).

In one embodiment, to determine or label pixel location data, thefeature module 201 can process the images using image recognition orequivalent to identify the pixels of each image corresponding to theselected intersection features/ground control points. In other words,following the identification of candidate feature points in severaltop-down images, corresponding image pixel locations are identified. Inone embodiment, for each real-world feature (e.g., line intersection),the corresponding pixel coordinates in two or more images are recorded,creating a pixel correspondence of the form {(u₁, v₂), (u₂, v₂), . . . }or equivalent. Here, u and v are pixel locations of the same physicalobject or feature depicted in the images (e.g., pixel locations alongthe x and y axis respectively of a pixel grid comprising the image), andthe subscript indicates in which image the feature is labeled.

FIGS. 6A and 6B are diagrams illustrating multiple images of the sameintersection feature, according to one embodiment. In this example, FIG.6A illustrates a first top-down image 601 that depicts an intersectionwith a crosswalk corner 603 in a main view 605 and a zoomed view 607,and FIG. 6B illustrates a second top-down image 621 of the sameintersection with the crosswalk corner 603 in the main view 605 and thezoomed view 607 captured at a different time. FIGS. 6A and 6B illustratean example of a crosswalk corner pixel correspondence (e.g., forcrosswalk corner 603) between two satellite image patches. In oneembodiment, the pixel correspondences, together with image metadata suchas but not limited to camera position, orientation, focal length, etc.can be used to estimate the 3D position of the intersectionfeature/ground control point (e.g., the crosswalk corner 603).

Once the definition of such ground control points is done, a large setof annotated or human-created observations (e.g., ground truth images ofintersections features) could be obtained. For example, to generate theground truth data, correspondences among detected points can bedetermined by human labelers (perhaps with visual aids to orient andco-register multiple images) or through automated means (brute-forcematching, approximate nearest neighbors, supervised deep neuralnetworks, etc.). The output of this process is a set of ground truthimages labeled with learnable map features/ground control points.

In step 305, the training module 203 can present this ground truth imagedata to a machine learning model of the machine learning system 107during training using, for instance, supervised deep convolutionalnetworks or equivalent. In other words, the training module 305 trains amachine learning model using the plurality of ground truth images toidentify learnable ground control points depicted in an input images.Generally, a machine learning model (e.g., a neural network, set ofequations, rules, decision trees, etc.) is trained to manipulate aninput feature set to make a prediction about the feature set or thephenomenon/observation that the feature set represents. In oneembodiment, the training features for the machine learning model includethe determined pixel correspondence or pixel location of the selectedmap features/ground control points in the ground truth images.

In one embodiment, because the ground truth images can originate fromany number of sources, the resolution, quality, etc. of each image canvary. For example, the resolution of top imagery of different satellitesor other aerial sources can vary depending on the kind of camera sensorsused. These different sensors then produce images with differentresolutions. This variance, in turn, can lead to uncertainty or error.Accordingly, the machine learning model can be further trained tocalculate an uncertainty associated with the predicted location based ona characteristic of said each of the plurality of images, a respectivesource of said each of the plurality of images, or a combination.

In one embodiment, the training module 203 can incorporate a supervisedlearning model (e.g., a logistic regression model, RandomForest model,and/or any equivalent model) to train a machine learning model using theground truth image data together with the labeled ground control points.For example, during training, the training module 203 uses a learnermodule that feeds images and derived feature sets (e.g., pixelcorrespondences, image attributes, etc.) into the machine learning modelto compute a predicted feature set (e.g., predicted ground controlpoints presented in input images and/or other characteristics of groundcontrol points) using an initial set of model parameters.

The learner module then compares the predicted feature set to the groundtruth data (e.g., images labeled with known ground control point pixellocations and/or attributes). For example, the learner module computes aloss function representing, for instance, an accuracy of the predictionsfor the initial set of model parameters. In one embodiment, the trainingmodule 203 computes a loss function for the training of the machinelearning module based on the ground truth images. The learner module ofthe training module 203 then incrementally adjusts the model parametersuntil the model minimizes the loss function (e.g., achieves a maximumaccuracy with respect to the manually marked labels). In other words, a“trained” feature prediction model is a classifier with model parametersadjusted to make accurate predictions with respect to the ground truthdata.

FIG. 7 is a flowchart of a process for predicting ground control pointusing in images using a trained machine learning model, according to oneembodiment. In various embodiments, the machine learning system 107and/or any of the modules 201-207 of the machine learning system 107 mayperform one or more portions of the process 300 and may be implementedin, for instance, a chip set including a processor and a memory as shownin FIG. 11. As such, the machine learning system 107 and/or the modules201-207 can provide means for accomplishing various parts of the process300, as well as means for accomplishing embodiments of other processesdescribed herein in conjunction with other components of the system 100.Although the process 300 is illustrated and described as a sequence ofsteps, its contemplated that various embodiments of the process 300 maybe performed in any order or combination and need not include all of theillustrated steps.

In one embodiment, the process 700 can be used to classify or identifyground control points in image data (e.g., top-down imagery) after amachine learning model of the machine learning system 107 is trained(e.g., according to the embodiments of the process 300 above).

In step 701, the prediction module 205 retrieves or is provided with oneor more input images that depict potential ground control points thatare learnable features (e.g., intersection features) according to theembodiments described above. In one embodiment, the input image datainclude top-down imagery (e.g., captured by cameras mounted onsatellites, airplanes, drones, and/or other aerial vehicles). Theprediction module 205 can process the input image data to identifyground control points (e.g., learnable intersection features). Theprocessing can include, for instance, using the computer vision system109 or equivalent to recognize pixels corresponding to thefeatures/ground control points of interest. The recognition process canidentify the pixel locations in each image corresponding to thefeatures/ground control points. If the input image data include multipleimages (e.g., two or more images) of the same feature or ground controlpoint, the prediction module 205 can create pixel correspondences of thefeature across the multiple images according to the embodimentsdescribed with respect to FIG. 3 above.

In step 703, the prediction module 205 creates an input vector, inputmatrix, or equivalent comprising the pixel locations/pixelcorrespondences extracted above along with any other features/attributesused to train the machine learning model. By way of example, these otherfeatures/attributes can include but is not limited to image itself,derived attributes of the images (e.g., resolution, exposure, cameraposition, focal length, camera type, etc.), the corresponding datasources (e.g., satellite, airplane, drone, etc.), contextual data at thetime of image capture (e.g., time, weather, etc.), and/or the like. Inone embodiment, creating the input vector includes converting theextracted features to a common format, normalizing values, removingoutliers, and/or any other known pre-processing step to reduce dataanomalies in the input data.

In step 705, the prediction module 205 processes the input vector usingthe trained machine learning model to output a predicted pixel locationof the ground control point/feature that is present in the input image.In one embodiment, the predicted ground control point data can alsoinclude a predicted uncertainty or error associated with the predictedground control point (e.g., error in predicted pixel locations of theground control point).

In one embodiment, the ground control points once predicted and obtainedusing machine learning could serve multiple purposes including but notlimited to evaluation of map data quality, absolute constraints for thepositional correction of data sources, serve as localization objects,etc. For example, in one embodiment, the triangulation module 207 candetermine pixel correspondences between the same ground control pointautomatically labeled in multiple images. The triangulation module 207can then use a triangulation process to estimate a real-world 3Dlocation of the ground control points from the pixel correspondences andcamera model or camera pose information. The triangulation process canuse the known camera location, pointing direction, and/or other cameraattributes (e.g., focal length, etc.) indicated in the camera model orpose information to triangulate the real-world location of the groundcontrol point from the known camera locations of different images inwhich the ground control is labeled (e.g., labeled according to theembodiments of the automated machine learning process described above).

FIG. 8 is a diagram illustrating a workflow for using ground controlpoints, according to one embodiment. As shown in the process 800 of FIG.8, a set of input images 801 (e.g., top-down imagery) is obtained, andthen processed using the trained machined learning model 803 (e.g., ofthe machine learning system 107) to generate ground control points(e.g., ground control point data 805). This ground control point data805 can then be provided to the mapping platform 103 and/or any othercomponent of the system 100 (e.g., services platform 111, services 113,vehicle 101, etc.) for the purposes listed above or other purposes thatrely on ground control points. Because capturing images is generally aless resource intensive process than deploying ground surveyors tomanually determine ground control points, the system 100 can capture aseries of input images 801 to cover a wider geographical area morefrequently for automated processing by the machine learning system 107.As a result, the embodiments described herein for determining groundcontrol points from image data using machine learning can advantageouslyprovide for scalable, relatively inexpensive, high accuracy groundcontrol point data 805 that is also easy to keep up-to-date (e.g.,thereby reducing invalid or obsolete ground control points).

In one embodiment, the embodiments of the machine learning system 107can be used to enable a variety of sophisticated services andapplications. For example, autonomous driving has quickly become an areaof intense interest where machine learning in combination with computervision systems can be used. One application of vision techniques inautonomous driving is localization of the vehicle 101 with respect toground control points (e.g., reference locations with highly accurateknown locations). In one embodiment, the system 100 (e.g., the mappingplatform 103) can generate ground control points according to theembodiments as described herein. These ground control points can then beused as reference markers by vehicles 101 to localize themselves.

Traditionally, most vehicle navigation systems have accomplished thislocalization using GPS, which generally provides a real-time locationwith a 95% confidence interval of 7.8 meters. However, in complicatedurban environments, reflection of GPS signals can further increase thiserror, such that one's location may be off by as much as 30 meters.Given that the width of many lanes is 3-4 meters, this accuracy is notsufficient to properly localize a vehicle 101 (e.g., an autonomousvehicle) so that it can make safe route planning decisions. Othersensors, such as inertial measurement units (IMUs) can increase theaccuracy of localization by taking into account vehicle movement, butthese sensors tend to drift and still do not provide sufficient accuracyfor localization.

In general, a localization accuracy of around 10 cm is needed for safedriving (e.g., autonomous driving) in many areas. One way to achievethis level of accuracy is to use visual odometry, in which features(e.g., ground control points) are detected from imagery using featureprediction models (i.e., a machine learning classifier). These featurescan then be matched to a database of ground control points to determineone's location. By way of example, traditional feature-basedlocalization that both detect features and localize against themgenerally rely on low-level features. However, low-level featurestypically used in these algorithms (e.g., Scale-Invariant FeatureTransform (SIFT) or Oriented FAST and rotated BRIEF (ORB)) tend to bebrittle and not persist in different environmental and lightingconditions. As a result, they often cannot be used to localize a vehicleon different days in different weather conditions. Aside fromreproducibility, the ability to detect and store higher level featuresof different types (e.g., ground control points based on intersectionfeatures such as lane markings, lane lines, etc.) can provide better andmore accurate localization.

A vehicle 101, for instance, can use computer vision to identify a knownground control point (e.g., a crosswalk corner), and then estimate itsdistance to the ground control point. Because the location of the groundcontrol point is known with high accuracy, the vehicle 101 can computeits distance to the ground control point to use as a distance offset tothe known location to localize itself with a corresponding high degreeof accuracy. Understanding one's location on a map enables planning of aroute, both on fine and coarse scales. On a coarse scale, navigationmaps allow vehicles 101 to know what roads to use to reach a particulardestination. However, on a finer scale, maps allow vehicles 101 to knowwhat lanes to be in and when to make lane changes. Knowing thisinformation is important for planning an efficient and safe route, forin complicated driving situations maneuvers need to be executed in atimely fashion, and sometimes before they are visually obvious. Inaddition, localization with respect to ground control points enables theincorporation of other real-time information into route planning. Suchinformation could include traffic, areas with unsafe driving conditions(ice, fog, potholes, e.g.), and temporary road changes likeconstruction.

Returning to FIG. 1, as shown, the system 100 includes the mappingplatform 103 which incorporates the machine learning system 107 fordetermining ground control points from image data according the variousembodiments described herein. In addition, the mapping platform 103 caninclude the computer vision system 109 configured to use machinelearning to detect objects or features (e.g., intersection features)depicted in images that can be used as ground control points. Forexample, with respect to autonomous, navigation, mapping, and/or othersimilar applications, the computer vision system 109 can detect groundcontrol points in input images and generate ground control point data(e.g., location data) and associated prediction confidencevalues/uncertainties, according to the various embodiments describedherein. In one embodiment, the machine learning system 107 includes aneural network or other machine learning system to make predictions frommachine learning models. For example, when the input to the machinelearning model are images used for visual odometry, the features ofinterest can include ground control points detected in the images tosupport localization of, e.g., a vehicle 101 or other similarapplications within the sensed environment. In one embodiment, theneural network of the machine learning system 107 is a traditionalconvolutional neural network which consists of multiple layers ofcollections of one or more neurons (which are configured to process aportion of an input image. In one embodiment, the receptive fields ofthese collections of neurons (e.g., a receptive layer) can be configuredto correspond to the area of an input image delineated by a respective agrid cell generated as described above.

In one embodiment, the machine learning system 107 and/or the computervision system 109 also have connectivity or access to a geographicdatabase 105 which stores the learned ground control points generatedaccording to the embodiments described herein. In one embodiment, thegeographic database 105 includes representations of mapped groundcontrol points and related geographic features to facilitate visualodometry to increase localization accuracy. In one embodiment, themachine learning system 107 and/or computer vision system 109 haveconnectivity over a communication network 119 to the services platform111 that provides one or more services 113. By way of example, theservices 113 may be third party services and include mapping services,navigation services, travel planning services, notification services,social networking services, content (e.g., audio, video, images, etc.)provisioning services, application services, storage services,contextual information determination services, location based services,information based services (e.g., weather, news, etc.), etc. In oneembodiment, the services 113 uses the output of the machine learningsystem 107 and/or of the computer vision system 109 (e.g., groundcontrol point data) to localize the vehicle 101 or UE 115 (e.g., aportable navigation device, smartphone, portable computer, tablet, etc.)to provide services 113 such as navigation, mapping, otherlocation-based services, etc.

In one embodiment, the machine learning system 107 and/or computervision system 109 may be a platform with multiple interconnectedcomponents. The machine learning system 107 and/or computer visionsystem 109 may include multiple servers, intelligent networking devices,computing devices, components and corresponding software for providingparametric representations of lane lines. In addition, it is noted thatthe machine learning system 107 and/or computer vision system 109 may bea separate entity of the system 100, a part of the one or more services113, a part of the services platform 111, or included within the UE 115and/or vehicle 101.

In one embodiment, content providers 121 a-121 m (collectively referredto as content providers 121) may provide content or data (e.g.,including geographic data, parametric representations of mappedfeatures, etc.) to the geographic database 105, the machine learningsystem 107, the computer vision system 109, the services platform 111,the services 113, the UE 115, the vehicle 101, and/or an application 117executing on the UE 115. The content provided may be any type ofcontent, such as map content, textual content, audio content, videocontent, image content, etc. In one embodiment, the content providers121 may provide content that may aid in the detecting and classifying oflane lines and/or other features in image data and estimating thequality of the detected features. In one embodiment, the contentproviders 121 may also store content associated with the geographicdatabase 105, machine learning system 107, computer vision system 109,services platform 111, services 113, UE 115, and/or vehicle 101. Inanother embodiment, the content providers 121 may manage access to acentral repository of data, and offer a consistent, standard interfaceto data, such as a repository of the geographic database 105.

In one embodiment, the UE 115 and/or vehicle 101 may execute a softwareapplication 117 to capture image data or other observation data fordetermining ground control points or using ground control pointsaccording the embodiments described herein. By way of example, theapplication 117 may also be any type of application that is executableon the UE 115 and/or vehicle 101, such as autonomous drivingapplications, mapping applications, location-based service applications,navigation applications, content provisioning services, camera/imagingapplication, media player applications, social networking applications,calendar applications, and the like. In one embodiment, the application117 may act as a client for the machine learning system 107 and/orcomputer vision system 109 and perform one or more functions associatedwith determining ground control points from image data alone or incombination with the machine learning system 107.

By way of example, the UE 115 is any type of embedded system, mobileterminal, fixed terminal, or portable terminal including a built-innavigation system, a personal navigation device, mobile handset,station, unit, device, multimedia computer, multimedia tablet, Internetnode, communicator, desktop computer, laptop computer, notebookcomputer, netbook computer, tablet computer, personal communicationsystem (PCS) device, personal digital assistants (PDAs), audio/videoplayer, digital camera/camcorder, positioning device, fitness device,television receiver, radio broadcast receiver, electronic book device,game device, or any combination thereof, including the accessories andperipherals of these devices, or any combination thereof. It is alsocontemplated that the UE 115 can support any type of interface to theuser (such as “wearable” circuitry, etc.). In one embodiment, the UE 115may be associated with the vehicle 101 or be a component part of thevehicle 101.

In one embodiment, the UE 115 and/or vehicle 101 are configured withvarious sensors for generating or collecting environmental image data(e.g., for processing by the machine learning system 107 and/or computervision system 109), related geographic data, etc. In one embodiment, thesensed data represent sensor data associated with a geographic locationor coordinates at which the sensor data was collected. By way ofexample, the sensors may include a global positioning sensor forgathering location data (e.g., GPS), a network detection sensor fordetecting wireless signals or receivers for different short-rangecommunications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication(NFC) etc.), temporal information sensors, a camera/imaging sensor forgathering image data (e.g., the camera sensors may automatically captureground control point imagery, etc. for analysis), an audio recorder forgathering audio data, velocity sensors mounted on steering wheels of thevehicles, switch sensors for determining whether one or more vehicleswitches are engaged, and the like.

Other examples of sensors of the UE 115 and/or vehicle 101 may includelight sensors, orientation sensors augmented with height sensors andacceleration sensor (e.g., an accelerometer can measure acceleration andcan be used to determine orientation of the vehicle), tilt sensors todetect the degree of incline or decline of the vehicle along a path oftravel, moisture sensors, pressure sensors, etc. In a further exampleembodiment, sensors about the perimeter of the UE 115 and/or vehicle 101may detect the relative distance of the vehicle from a lane or roadway,the presence of other vehicles, pedestrians, traffic lights, potholesand any other objects, or a combination thereof. In one scenario, thesensors may detect weather data, traffic information, or a combinationthereof. In one embodiment, the UE 115 and/or vehicle 101 may includeGPS or other satellite-based receivers to obtain geographic coordinatesfrom satellites 123 for determining current location and time. Further,the location can be determined by visual odometry, triangulation systemssuch as A-GPS, Cell of Origin, or other location extrapolationtechnologies. In yet another embodiment, the sensors can determine thestatus of various control elements of the car, such as activation ofwipers, use of a brake pedal, use of an acceleration pedal, angle of thesteering wheel, activation of hazard lights, activation of head lights,etc.

In one embodiment, the communication network 119 of system 100 includesone or more networks such as a data network, a wireless network, atelephony network, or any combination thereof. It is contemplated thatthe data network may be any local area network (LAN), metropolitan areanetwork (MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks, codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®,Internet Protocol (IP) data casting, satellite, mobile ad-hoc network(MANET), and the like, or any combination thereof.

By way of example, the machine learning system 107, computer visionsystem 109, services platform 111, services 113, UE 115, vehicle 101,and/or content providers 121 communicate with each other and othercomponents of the system 100 using well known, new or still developingprotocols. In this context, a protocol includes a set of rules defininghow the network nodes within the communication network 119 interact witheach other based on information sent over the communication links. Theprotocols are effective at different layers of operation within eachnode, from generating and receiving physical signals of various types,to selecting a link for transferring those signals, to the format ofinformation indicated by those signals, to identifying which softwareapplication executing on a computer system sends or receives theinformation. The conceptually different layers of protocols forexchanging information over a network are described in the Open SystemsInterconnection (OSI) Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 9 is a diagram of a geographic database, according to oneembodiment. In one embodiment, the geographic database 105 includesgeographic data 901 used for (or configured to be compiled to be usedfor) mapping and/or navigation-related services, such as for videoodometry based on the mapped features (e.g., lane lines, road markings,signs, etc.). In one embodiment, the geographic database 105 includeshigh resolution or high definition (HD) mapping data that providecentimeter-level or better accuracy of map features. For example, thegeographic database 105 can be based on Light Detection and Ranging(LiDAR) or equivalent technology to collect billions of 3D points andmodel road surfaces and other map features down to the number lanes andtheir widths. In one embodiment, the HD mapping data (e.g., HD datarecords 911) capture and store details such as the slope and curvatureof the road, lane markings, roadside objects such as sign posts,including what the signage denotes. By way of example, the HD mappingdata enable highly automated vehicles to precisely localize themselveson the road.

In one embodiment, geographic features (e.g., two-dimensional orthree-dimensional features) are represented using polygons (e.g.,two-dimensional features) or polygon extrusions (e.g., three-dimensionalfeatures). For example, the edges of the polygons correspond to theboundaries or edges of the respective geographic feature. In the case ofa building, a two-dimensional polygon can be used to represent afootprint of the building, and a three-dimensional polygon extrusion canbe used to represent the three-dimensional surfaces of the building. Itis contemplated that although various embodiments are discussed withrespect to two-dimensional polygons, it is contemplated that theembodiments are also applicable to three-dimensional polygon extrusions.Accordingly, the terms polygons and polygon extrusions as used hereincan be used interchangeably.

In one embodiment, the following terminology applies to therepresentation of geographic features in the geographic database 105.

“Node”—A point that terminates a link.

“Line segment”—A straight line connecting two points.

“Link” (or “edge”)—A contiguous, non-branching string of one or moreline segments terminating in a node at each end.

“Shape point”—A point along a link between two nodes (e.g., used toalter a shape of the link without defining new nodes).

“Oriented link”—A link that has a starting node (referred to as the“reference node”) and an ending node (referred to as the “non referencenode”).

“Simple polygon”—An interior area of an outer boundary formed by astring of oriented links that begins and ends in one node. In oneembodiment, a simple polygon does not cross itself.

“Polygon”—An area bounded by an outer boundary and none or at least oneinterior boundary (e.g., a hole or island). In one embodiment, a polygonis constructed from one outer simple polygon and none or at least oneinner simple polygon. A polygon is simple if it just consists of onesimple polygon, or complex if it has at least one inner simple polygon.

In one embodiment, the geographic database 105 follows certainconventions. For example, links do not cross themselves and do not crosseach other except at a node. Also, there are no duplicated shape points,nodes, or links. Two links that connect each other have a common node.In the geographic database 105, overlapping geographic features arerepresented by overlapping polygons. When polygons overlap, the boundaryof one polygon crosses the boundary of the other polygon. In thegeographic database 105, the location at which the boundary of onepolygon intersects they boundary of another polygon is represented by anode. In one embodiment, a node may be used to represent other locationsalong the boundary of a polygon than a location at which the boundary ofthe polygon intersects the boundary of another polygon. In oneembodiment, a shape point is not used to represent a point at which theboundary of a polygon intersects the boundary of another polygon.

As shown, the geographic database 105 includes node data records 903,road segment or link data records 905, POI data records 907, groundcontrol point data records 909, HD mapping data records 911, and indexes913, for example. More, fewer or different data records can be provided.In one embodiment, additional data records (not shown) can includecartographic (“carto”) data records, routing data, and maneuver data. Inone embodiment, the indexes 913 may improve the speed of data retrievaloperations in the geographic database 105. In one embodiment, theindexes 913 may be used to quickly locate data without having to searchevery row in the geographic database 105 every time it is accessed. Forexample, in one embodiment, the indexes 913 can be a spatial index ofthe polygon points associated with stored feature polygons.

In exemplary embodiments, the road segment data records 905 are links orsegments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes. The node data records 903 are end pointscorresponding to the respective links or segments of the road segmentdata records 905. The road link data records 905 and the node datarecords 903 represent a road network, such as used by vehicles, cars,and/or other entities. Alternatively, the geographic database 105 cancontain path segment and node data records or other data that representpedestrian paths or areas in addition to or instead of the vehicle roadrecord data, for example.

The road/link segments and nodes can be associated with attributes, suchas functional class, a road elevation, a speed category, a presence orabsence of road features, geographic coordinates, street names, addressranges, speed limits, turn restrictions at intersections, and othernavigation related attributes, as well as POIs, such as gasolinestations, hotels, restaurants, museums, stadiums, offices, automobiledealerships, auto repair shops, buildings, stores, parks, etc. Thegeographic database 105 can include data about the POIs and theirrespective locations in the POI data records 907. The geographicdatabase 105 can also include data about places, such as cities, towns,or other communities, and other geographic features, such as bodies ofwater, mountain ranges, etc. Such place or feature data can be part ofthe POI data records 907 or can be associated with POIs or POI datarecords 907 (such as a data point used for displaying or representing aposition of a city).

In one embodiment, the geographic database 105 can also include groundcontrol point data records 909 for storing the ground control pointdata, learnable map features, as well as other related data usedaccording to the various embodiments described herein. In addition, theground control point data records 909 can also store ground truthtraining and evaluation data, machine learning models, annotatedobservations, and/or any other data generated or used by the system 100according to the various embodiments described herein. By way ofexample, the ground control point data records 909 can be associatedwith one or more of the node records 903, road segment records 905,and/or POI data records 907 to support localization or visual odometrybased on the features stored therein and the corresponding estimatedquality of the features. In this way, the records 909 can also beassociated with or used to classify the characteristics or metadata ofthe corresponding records 903, 905, and/or 907.

In one embodiment, as discussed above, the HD mapping data records 911model road surfaces and other map features to centimeter-level or betteraccuracy. The HD mapping data records 911 also include lane models thatprovide the precise lane geometry with lane boundaries, as well as richattributes of the lane models. These rich attributes include, but arenot limited to, lane traversal information, lane types, lane markingtypes, lane level speed limit information, and/or the like. In oneembodiment, the HD mapping data records 911 are divided into spatialpartitions of varying sizes to provide HD mapping data to vehicles 101and other end user devices with near real-time speed without overloadingthe available resources of the vehicles 101 and/or devices (e.g.,computational, memory, bandwidth, etc. resources).

In one embodiment, the HD mapping data records 911 are created fromhigh-resolution 3D mesh or point-cloud data generated, for instance,from LiDAR-equipped vehicles. The 3D mesh or point-cloud data areprocessed to create 3D representations of a street or geographicenvironment at centimeter-level accuracy for storage in the HD mappingdata records 911.

In one embodiment, the HD mapping data records 911 also includereal-time sensor data collected from probe vehicles in the field. Thereal-time sensor data, for instance, integrates real-time trafficinformation, weather, and road conditions (e.g., potholes, roadfriction, road wear, etc.) with highly detailed 3D representations ofstreet and geographic features to provide precise real-time also atcentimeter-level accuracy. Other sensor data can include vehicletelemetry or operational data such as windshield wiper activation state,braking state, steering angle, accelerator position, and/or the like.

In one embodiment, the geographic database 105 can be maintained by thecontent provider 121 in association with the services platform 111(e.g., a map developer). The map developer can collect geographic datato generate and enhance the geographic database 105. There can bedifferent ways used by the map developer to collect data. These ways caninclude obtaining data from other sources, such as municipalities orrespective geographic authorities. In addition, the map developer canemploy field personnel to travel by vehicle (e.g., vehicle 101 and/or UE115) along roads throughout the geographic region to observe featuresand/or record information about them, for example. Also, remote sensing,such as aerial or satellite photography, can be used.

The geographic database 105 can be a master geographic database storedin a format that facilitates updating, maintenance, and development. Forexample, the master geographic database or data in the master geographicdatabase can be in an Oracle spatial format or other spatial format,such as for development or production purposes. The Oracle spatialformat or development/production database can be compiled into adelivery format, such as a geographic data files (GDF) format. The datain the production and/or delivery formats can be compiled or furthercompiled to form geographic database products or databases, which can beused in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by a vehicle 101 or UE 115, for example. Thenavigation-related functions can correspond to vehicle navigation,pedestrian navigation, or other types of navigation. The compilation toproduce the end user databases can be performed by a party or entityseparate from the map developer. For example, a customer of the mapdeveloper, such as a navigation device developer or other end userdevice developer, can perform compilation on a received geographicdatabase in a delivery format to produce one or more compiled navigationdatabases.

The processes described herein for determining ground control pointsfrom image data may be advantageously implemented via software, hardware(e.g., general processor, Digital Signal Processing (DSP) chip, anApplication Specific Integrated Circuit (ASIC), Field Programmable GateArrays (FPGAs), etc.), firmware or a combination thereof. Such exemplaryhardware for performing the described functions is detailed below.

FIG. 10 illustrates a computer system 1000 upon which an embodiment ofthe invention may be implemented. Computer system 1000 is programmed(e.g., via computer program code or instructions) to determine groundcontrol points from image data as described herein and includes acommunication mechanism such as a bus 1010 for passing informationbetween other internal and external components of the computer system1000. Information (also called data) is represented as a physicalexpression of a measurable phenomenon, typically electric voltages, butincluding, in other embodiments, such phenomena as magnetic,electromagnetic, pressure, chemical, biological, molecular, atomic,sub-atomic and quantum interactions. For example, north and southmagnetic fields, or a zero and non-zero electric voltage, represent twostates (0, 1) of a binary digit (bit). Other phenomena can representdigits of a higher base. A superposition of multiple simultaneousquantum states before measurement represents a quantum bit (qubit). Asequence of one or more digits constitutes digital data that is used torepresent a number or code for a character. In some embodiments,information called analog data is represented by a near continuum ofmeasurable values within a particular range.

A bus 1010 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus1010. One or more processors 1002 for processing information are coupledwith the bus 1010.

A processor 1002 performs a set of operations on information asspecified by computer program code related to determining ground controlpoints from image data. The computer program code is a set ofinstructions or statements providing instructions for the operation ofthe processor and/or the computer system to perform specified functions.The code, for example, may be written in a computer programming languagethat is compiled into a native instruction set of the processor. Thecode may also be written directly using the native instruction set(e.g., machine language). The set of operations include bringinginformation in from the bus 1010 and placing information on the bus1010. The set of operations also typically include comparing two or moreunits of information, shifting positions of units of information, andcombining two or more units of information, such as by addition ormultiplication or logical operations like OR, exclusive OR (XOR), andAND. Each operation of the set of operations that can be performed bythe processor is represented to the processor by information calledinstructions, such as an operation code of one or more digits. Asequence of operations to be executed by the processor 1002, such as asequence of operation codes, constitute processor instructions, alsocalled computer system instructions or, simply, computer instructions.Processors may be implemented as mechanical, electrical, magnetic,optical, chemical or quantum components, among others, alone or incombination.

Computer system 1000 also includes a memory 1004 coupled to bus 1010.The memory 1004, such as a random access memory (RAM) or other dynamicstorage device, stores information including processor instructions fordetermining ground control points from image data. Dynamic memory allowsinformation stored therein to be changed by the computer system 1000.RAM allows a unit of information stored at a location called a memoryaddress to be stored and retrieved independently of information atneighboring addresses. The memory 1004 is also used by the processor1002 to store temporary values during execution of processorinstructions. The computer system 1000 also includes a read only memory(ROM) 1006 or other static storage device coupled to the bus 1010 forstoring static information, including instructions, that is not changedby the computer system 1000. Some memory is composed of volatile storagethat loses the information stored thereon when power is lost. Alsocoupled to bus 1010 is a non-volatile (persistent) storage device 1008,such as a magnetic disk, optical disk or flash card, for storinginformation, including instructions, that persists even when thecomputer system 1000 is turned off or otherwise loses power.

Information, including instructions for determining ground controlpoints from image data, is provided to the bus 1010 for use by theprocessor from an external input device 1012, such as a keyboardcontaining alphanumeric keys operated by a human user, or a sensor. Asensor detects conditions in its vicinity and transforms thosedetections into physical expression compatible with the measurablephenomenon used to represent information in computer system 1000. Otherexternal devices coupled to bus 1010, used primarily for interactingwith humans, include a display device 1014, such as a cathode ray tube(CRT) or a liquid crystal display (LCD), or plasma screen or printer forpresenting text or images, and a pointing device 1016, such as a mouseor a trackball or cursor direction keys, or motion sensor, forcontrolling a position of a small cursor image presented on the display1014 and issuing commands associated with graphical elements presentedon the display 1014. In some embodiments, for example, in embodiments inwhich the computer system 1000 performs all functions automaticallywithout human input, one or more of external input device 1012, displaydevice 1014 and pointing device 1016 is omitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 1020, is coupled to bus1010. The special purpose hardware is configured to perform operationsnot performed by processor 1002 quickly enough for special purposes.Examples of application specific ICs include graphics accelerator cardsfor generating images for display 1014, cryptographic boards forencrypting and decrypting messages sent over a network, speechrecognition, and interfaces to special external devices, such as roboticarms and medical scanning equipment that repeatedly perform some complexsequence of operations that are more efficiently implemented inhardware.

Computer system 1000 also includes one or more instances of acommunications interface 1070 coupled to bus 1010. Communicationinterface 1070 provides a one-way or two-way communication coupling to avariety of external devices that operate with their own processors, suchas printers, scanners and external disks. In general the coupling iswith a network link 1078 that is connected to a local network 1080 towhich a variety of external devices with their own processors areconnected. For example, communication interface 1070 may be a parallelport or a serial port or a universal serial bus (USB) port on a personalcomputer. In some embodiments, communications interface 1070 is anintegrated services digital network (ISDN) card or a digital subscriberline (DSL) card or a telephone modem that provides an informationcommunication connection to a corresponding type of telephone line. Insome embodiments, a communication interface 1070 is a cable modem thatconverts signals on bus 1010 into signals for a communication connectionover a coaxial cable or into optical signals for a communicationconnection over a fiber optic cable. As another example, communicationsinterface 1070 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN, such as Ethernet. Wirelesslinks may also be implemented. For wireless links, the communicationsinterface 1070 sends or receives or both sends and receives electrical,acoustic or electromagnetic signals, including infrared and opticalsignals, that carry information streams, such as digital data. Forexample, in wireless handheld devices, such as mobile telephones likecell phones, the communications interface 1070 includes a radio bandelectromagnetic transmitter and receiver called a radio transceiver. Incertain embodiments, the communications interface 1070 enablesconnection to the communication network 119 for determining groundcontrol points from image data.

The term computer-readable medium is used herein to refer to any mediumthat participates in providing information to processor 1002, includinginstructions for execution. Such a medium may take many forms,including, but not limited to, non-volatile media, volatile media andtransmission media. Non-volatile media include, for example, optical ormagnetic disks, such as storage device 1008. Volatile media include, forexample, dynamic memory 1004.

Transmission media include, for example, coaxial cables, copper wire,fiber optic cables, and carrier waves that travel through space withoutwires or cables, such as acoustic waves and electromagnetic waves,including radio, optical and infrared waves. Signals include man-madetransient variations in amplitude, frequency, phase, polarization orother physical properties transmitted through the transmission media.Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, any other magneticmedium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards,paper tape, optical mark sheets, any other physical medium with patternsof holes or other optically recognizable indicia, a RAM, a PROM, anEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrierwave, or any other medium from which a computer can read.

FIG. 11 illustrates a chip set 1100 upon which an embodiment of theinvention may be implemented. Chip set 1100 is programmed to determineground control points from image data as described herein and includes,for instance, the processor and memory components described with respectto FIG. 10 incorporated in one or more physical packages (e.g., chips).By way of example, a physical package includes an arrangement of one ormore materials, components, and/or wires on a structural assembly (e.g.,a baseboard) to provide one or more characteristics such as physicalstrength, conservation of size, and/or limitation of electricalinteraction. It is contemplated that in certain embodiments the chip setcan be implemented in a single chip.

In one embodiment, the chip set 1100 includes a communication mechanismsuch as a bus 1101 for passing information among the components of thechip set 1100. A processor 1103 has connectivity to the bus 1101 toexecute instructions and process information stored in, for example, amemory 1105. The processor 1103 may include one or more processing coreswith each core configured to perform independently. A multi-coreprocessor enables multiprocessing within a single physical package.Examples of a multi-core processor include two, four, eight, or greaternumbers of processing cores. Alternatively or in addition, the processor1103 may include one or more microprocessors configured in tandem viathe bus 1101 to enable independent execution of instructions,pipelining, and multithreading. The processor 1103 may also beaccompanied with one or more specialized components to perform certainprocessing functions and tasks such as one or more digital signalprocessors (DSP) 1107, or one or more application-specific integratedcircuits (ASIC) 1109. A DSP 1107 typically is configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1103. Similarly, an ASIC 1109 can be configured to performedspecialized functions not easily performed by a general purposedprocessor. Other specialized components to aid in performing theinventive functions described herein include one or more fieldprogrammable gate arrays (FPGA) (not shown), one or more controllers(not shown), or one or more other special-purpose computer chips.

The processor 1103 and accompanying components have connectivity to thememory 1105 via the bus 1101. The memory 1105 includes both dynamicmemory (e.g., RAM, magnetic disk, writable optical disk, etc.) andstatic memory (e.g., ROM, CD-ROM, etc.) for storing executableinstructions that when executed perform the inventive steps describedherein to determine ground control points from image data. The memory1105 also stores the data associated with or generated by the executionof the inventive steps.

FIG. 12 is a diagram of exemplary components of a mobile terminal (e.g.,UE 115 or embedded component of the vehicle 101) capable of operating inthe system of FIG. 1, according to one embodiment. Generally, a radioreceiver is often defined in terms of front-end and back-endcharacteristics. The front-end of the receiver encompasses all of theRadio Frequency (RF) circuitry whereas the back-end encompasses all ofthe base-band processing circuitry. Pertinent internal components of thetelephone include a Main Control Unit (MCU) 1203, a Digital SignalProcessor (DSP) 1205, and a receiver/transmitter unit including amicrophone gain control unit and a speaker gain control unit. A maindisplay unit 1207 provides a display to the user in support of variousapplications and mobile station functions that offer automatic contactmatching. An audio function circuitry 1209 includes a microphone 1211and microphone amplifier that amplifies the speech signal output fromthe microphone 1211. The amplified speech signal output from themicrophone 1211 is fed to a coder/decoder (CODEC) 1213.

A radio section 1215 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1217. The power amplifier (PA) 1219and the transmitter/modulation circuitry are operationally responsive tothe MCU 1203, with an output from the PA 1219 coupled to the duplexer1221 or circulator or antenna switch, as known in the art. The PA 1219also couples to a battery interface and power control unit 1220.

In use, a user of mobile station 1201 speaks into the microphone 1211and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1223. The control unit 1203 routes the digital signal into the DSP 1205for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as global evolution (EDGE), general packetradio service (GPRS), global system for mobile communications (GSM),Internet protocol multimedia subsystem (IMS), universal mobiletelecommunications system (UMTS), etc., as well as any other suitablewireless medium, e.g., microwave access (WiMAX), Long Term Evolution(LTE) networks, code division multiple access (CDMA), wireless fidelity(WiFi), satellite, and the like.

The encoded signals are then routed to an equalizer 1225 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1227 combines the signalwith a RF signal generated in the RF interface 1229. The modulator 1227generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1231 combinesthe sine wave output from the modulator 1227 with another sine wavegenerated by a synthesizer 1233 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1219 to increase thesignal to an appropriate power level. In practical systems, the PA 1219acts as a variable gain amplifier whose gain is controlled by the DSP1205 from information received from a network base station. The signalis then filtered within the duplexer 1221 and optionally sent to anantenna coupler 1235 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1217 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile station 1201 are received viaantenna 1217 and immediately amplified by a low noise amplifier (LNA)1237. A down-converter 1239 lowers the carrier frequency while thedemodulator 1241 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1225 and is processed by theDSP 1205. A Digital to Analog Converter (DAC) 1243 converts the signaland the resulting output is transmitted to the user through the speaker1245, all under control of a Main Control Unit (MCU) 1203—which can beimplemented as a Central Processing Unit (CPU) (not shown).

The MCU 1203 receives various signals including input signals from thekeyboard 1247. The keyboard 1247 and/or the MCU 1203 in combination withother user input components (e.g., the microphone 1211) comprise a userinterface circuitry for managing user input. The MCU 1203 runs a userinterface software to facilitate user control of at least some functionsof the mobile station 1201 to determine ground control points from imagedata. The MCU 1203 also delivers a display command and a switch commandto the display 1207 and to the speech output switching controller,respectively. Further, the MCU 1203 exchanges information with the DSP1205 and can access an optionally incorporated SIM card 1249 and amemory 1251. In addition, the MCU 1203 executes various controlfunctions required of the station. The DSP 1205 may, depending upon theimplementation, perform any of a variety of conventional digitalprocessing functions on the voice signals. Additionally, DSP 1205determines the background noise level of the local environment from thesignals detected by microphone 1211 and sets the gain of microphone 1211to a level selected to compensate for the natural tendency of the userof the mobile station 1201.

The CODEC 1213 includes the ADC 1223 and DAC 1243. The memory 1251stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable computer-readable storagemedium known in the art including non-transitory computer-readablestorage medium. For example, the memory device 1251 may be, but notlimited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage,or any other non-volatile or non-transitory storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1249 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1249 serves primarily to identify the mobile station 1201 on aradio network. The card 1249 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile station settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

What is claimed is:
 1. A computer-implemented method for determining aground control point from image data comprising: receiving inputspecifying an evaluation of a plurality of candidate features forselection as a ground control point from the plurality of candidatefeatures; determining, from the candidate feature, a feature that meetsa criteria indicative of a suitability for machine learning, wherein thecriteria comprises two or more selected from the group consisting of aconsistent definition, a unique identifiability, a spatial sparsity, ora geographic generalizability; selecting the feature as the groundcontrol point; retrieving a plurality of ground truth images depictingthe feature, wherein the plurality of ground truth images is labeledwith known pixel location data of the feature as respectively depictedin each of the plurality of ground truth images; and training a machinelearning model using the plurality of ground truth images to identifypredicted pixel location data of the ground control point as depicted inan input image.
 2. The method of claim 1, wherein the feature isselected based on determining that: the unique identifiability criteriaindicate that the feature is classifiable under one category, or theconsistent definition criteria indicate that the feature is consistentlyapplied and learned.
 3. The method of claim 1, wherein the feature isselected based on determining that an intersection feature has spatialsparsity that meets a sparsity criterion.
 4. The method of claim 1,wherein the feature is selected based on determining that the geographicgeneralizability criteria indicate that the feature is applicable to aplurality of different geographic regions.
 5. The method of claim 1,wherein the feature is selected from a category of curvilinear geometryfeatures or intersection features including a gore point, a cross-walkcorner, a boundary of a lane, and a limit line, or a combinationthereof.
 6. The method of claim 1, wherein feature is a visible featureof a roadway intersection that is visible from a top-down imageryperspective.
 7. The method of claim 1, wherein the training features forthe machine learning model include a pixel location of an intersectionfeature in said each of the plurality of ground truth images, a cameraposition of said each of the plurality of ground truth images, anorientation of said each of the plurality of ground truth images, afocal length of said each of the plurality of ground truth images, or acombination thereof.
 8. The method of claim 1, further comprising:processing a plurality of input images using the trained machinelearning model to identify that the ground control point is present inthe plurality of input images; and triangulating a real-world locationof the ground control point based on a pixel correspondence of theground control point among the plurality of input images.
 9. The methodof claim 8, wherein the real-world location is a three-dimensionallocation of the ground control point.
 10. The method of claim 1, whereinthe machine learning model is further trained to calculate anuncertainty associated with the predicted location based on acharacteristic of said each of a plurality of images, a respectivesource of said each of the plurality of images, or a combination.
 11. Anapparatus for determining a ground control point from image datacomprising: at least one processor; and at least one memory includingcomputer program code for one or more programs, the at least one memoryand the computer program code configured to, with the at least oneprocessor, cause the apparatus to perform at least the following,receive input specifying an evaluation of a plurality of candidatefeatures for selection as a ground control point from the plurality ofcandidate features; determine, from the candidate feature, a featurethat meets a criteria indicative of a suitability for machine learning,wherein the criteria comprises two or more selected from the groupconsisting of a consistent definition, a unique identifiability, aspatial sparsity, or a geographic generalizability; select the featureas the ground control point; retrieve a plurality of ground truth imagesdepicting the feature, wherein the plurality of ground truth images islabeled with known pixel location data of the feature as respectivelydepicted in each of the plurality of ground truth images; and train amachine learning model using the plurality of ground truth images toidentify predicted pixel location data of the ground control point asdepicted in an input image.
 12. The apparatus of claim 11, wherein thefeature is selected based on determining that: the uniqueidentifiability criteria indicate that the feature is classifiable underone category, or the consistent definition criteria indicate that thefeature is consistently applied and learned.
 13. The apparatus of claim11, wherein the feature is selected based on determining that anintersection feature has spatial sparsity that meets a sparsitycriterion.
 14. The apparatus of claim 11, wherein the feature isselected based on determining that geographic generalizability criteriaindicate that the feature is applicable to a plurality of differentgeographic regions.
 15. The apparatus of claim 11, wherein the featureis selected from a category of curvilinear geometry features orintersection features including a gore point, a cross-walk corner, aboundary of a lane, and a limit line, or a combination thereof.
 16. Anon-transitory computer-readable storage medium for determining a groundcontrol point from image data, carrying one or more sequences of one ormore instructions which, when executed by one or more processors, causean apparatus to perform: receiving input specifying an evaluation of aplurality of candidate features for selection as a ground control pointfrom the plurality of candidate features; determining, from thecandidate feature, a feature that meets a criteria indicative of asuitability for machine learning, wherein the criteria comprises two ormore selected from the group consisting of a consistent definition, aunique identifiability, a spatial sparsity, or a geographicgeneralizability; selecting the feature as the ground control point;retrieving a plurality of ground truth images depicting the feature,wherein the plurality of ground truth images is labeled with known pixellocation data of the feature as respectively depicted in each of theplurality of ground truth images; and training a machine learning modelusing the plurality of ground truth images to identify predicted pixellocation data of the ground control point as depicted in an input image.17. The non-transitory computer-readable storage medium of claim 16,wherein the feature is selected based on determining that: the uniqueidentifiability criteria indicate that an intersection feature isuniquely identifiable from among other intersection features, or theconsistent definition criteria indicate that the feature is consistentlyapplied and learned.
 18. The non-transitory computer-readable storagemedium of claim 16, wherein the feature is selected based on determiningthat an intersection feature has spatial sparsity that meets a sparsitycriterion.
 19. The non-transitory computer-readable storage medium ofclaim 16, wherein the feature is selected based on determining that thegeographic generalizability criteria indicate that the feature isapplicable to a plurality of different geographic regions.
 20. Thenon-transitory computer-readable storage medium of claim 16, wherein thefeature is selected from a category of curvilinear geometry features orintersection features including a gore point, a cross-walk corner, aboundary of a lane, and a limit line, or a combination thereof.